Contrast enhancement is often needed for image and video capture applications due to lens configurations, haze, or fog. Possible scenarios where contrast enhancements are needed include a vehicle driving down a foggy road or a surveillance camera viewing through a fog layer near the ocean. To obtain higher contrast imagery immediately, each image or frame from a video stream (un-compressed) is needed to be enhanced in near-real time.
In the case of foggy images, single image contrast enhancement attempts to estimate the amount of fog that exists in an image and then removes the fog, resulting in a contrast enhanced image. A common problem with fog in scenes is that the amount of contrast degradation is spatially varying. This can be seen in everyday life by observing the amount of haze present in front of two distant mountains. The furthest mountain appears hazier. This effect has been an important technique by artists for conveying scene depth on a two dimensional canvas.
One attempt to solving the contrast problem is to employ Histogram Equalization (HE), a statistical contrast enhancement method where the probability distribution function (PDF) of the image is transformed to be more uniform. Another similar approach is to enforce Gray-Level-Grouping, which also achieves spreading the original PDF while reserving dark and/or light pixels. However, these statistical methods attempt to achieve a desired PDF structure for an image without knowledge of the scene's physical parameters. A need exists for a contrast enhancement method that takes advantage of prior knowledge of an image scene.